Listwise Reranking in Production: Comparing Pointwise, Pairwise, and Listwise LLM Architectures, Sliding-Window Permutations, and Serving Economics
Information retrieval systems in production Retrieval-Augmented Generation (RAG) and enterprise search have transitioned through multiple reranking paradigms. While first-stage retrieval (dense vector embeddings and sparse lexical BM25/SPLADE) retrieves candidate sets of 50 to 200 documents in under 20 milliseconds, the precision of downstream generation depends heavily on the reranking stage. Traditional neural rerankers evaluate candidates through pointwise scoring or pairwise classification.
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